Abstract: Data mining is used to extract important knowledge from large datasets but sometimes these datasets are split among various parties. Association rule mining is one of the data mining technique used in distributed databases. This technique disclose some interesting relationship between locally large and globally large item sets and proposes an algorithm, fast distributed mining of association rules (FDM) which is an unsecured distributed version of the Apriori algorithm used to generates a small number of candidate sets and substantially reduces the number of messages to be passed at mining association rules. The main ingredient in proposed protocol is two novel secure multi party algorithm-one that computes the union of private subsets that each of the interacting player holds and another that test the inclusion of an element held by one player in a subset held by another. This protocol offers enhanced privacy with respect to the protocol. In addition, it is simpler and significantly more efficient in terms of communication rounds, communication cost and computational cost.
E. Gokulakannan and K. Venkatachalapathy, 2016. To Reduce Data Leakage in Horizontally Distributed Database Using Association Rules. Research Journal of Applied Sciences, 11: 215-220.